E-mail still proves to be very popular and an efficient communication tool. Due to its misuse, however, managing e-mails problem for organizations and individuals. Spam, known as unwanted message, is an example of misuse. Specifically, spam is defined as the arrival of unwelcomed bulk email not being requested for by recipients. This paper compares different Machine Learning Techniques classification of spam e-mails. Random Forest (RF), C4.5 and Artificial Neural Network (ANN) were tested to determine which method provides the best results in spam e-mail classification. Our results show that RF is the best technique applied on dataset Labs, indicating that ensemble methods may have an edge in spam detection effective susceptible to is spam, also is defined messages not istaken with or religious he most email by a . Furthermore, by spam. around (which makes (Grant, 2003; Every e-mail user in America received an average of 2200 pieces of spam e-mails in 2002. In 2007 it reached 3600 pieces of spam e-mails due to increase rate of 2% per month conducted a survey revealing that a Chinese spam e-mails weekly. Due to spam e enterprises lose up to 9 billion yearly reveal that spam e-mails take about 60% of the incomin in a corporate network. With inappropriate or no countermeasures, the situation will worsen and, in the end, spam e-mails may destruct the usage of e countries are slowly starting to use anti (Gaikwad & Halkarnikar, 2014). The main argument supporting spam increase is the fact that spammers do not have any costs for it: “Because email technology allows spammers to shift the costs almost entirely to third parties, there is no incentive for the spammers to reduce the volume” (Hann, Hui, Lai, Lee, & Png, 2006) issue for spam is the annoying content they carry significant amount of spam contains some offensive materials (Maria & Ng, 2009). In China, some specialists suggest spam email measure as early as possible. However, because of 1210 Sarajevo,

Epileptic foci localization is a crucial step in planning surgical treatment of medically intractable epilepsy. The solution to this problem can be determined by the detection of the earliest time of seizure onset in electroencephalographic (EEG) recordings. This study presents the application of support vector machine (SVM) for localization of the focus region at the epileptic seizure on the basis of EEG signals. used intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. We have been investigating a localization of the focus region at the epileptic seizure based on SVM to detect the onset of seizure activity in EEG data. The SVM is trained on sets of intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. The performance of SVM is measured by using accuracy obtained from a fit between the target value and network output. Our EEG based localization of the focus region at the epileptic seizure approach achieves 97.4% accuracy with using 10 fold cross validation. Therefore, our method can be successfully applied to localization of the epileptogenic foci.

Epileptic foci localization is a crucial step in planning surgical treatment of medically intractable epilepsy. The solution to this problem can be determined by the detection of the earliest time of seizure onset in electroencephalographic (EEG) recordings. This study presents the application of support vector machine (SVM) for localization of the focus region at the epileptic seizure on the basis of EEG signals. used intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. We have been investigating a localization of the focus region at the epileptic seizure based on SVM to detect the onset of seizure activity in EEG data. The SVM is trained on sets of intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. The performance of SVM is measured by using accuracy obtained from a fit between the target value and network output. Our EEG based localization of the focus region at the epileptic seizure approach achieves 97.4% accuracy with using 10 fold cross validation. Therefore, our method can be successfully applied to localization of the epileptogenic foci.